Compressed air makes up an important part of South African precious metal mining processes. Rising operational costs in the struggling mining sector increased the interest of the power utility, Eskom, and mine management in achievable electrical energy savings. Demand side management initiatives, funded by Eskom, realised a significant improvement in electrical energy efficiency of compressed air networks. Supply side interventions further aided optimisation by lowering operational costs. Previous research identified the need for integrating compressed air supply and demand side initiatives. Automated compressor control systems were needed in industry to realise missed opportunities due to human error on manual control systems. Automatic systems were found to be implemented in the industry, but missed savings opportunities were still encountered. This was due to the static nature of these control systems, requiring human intervention from skilled artisans. A comprehensive system is required that can adjust dynamically to the ever-changing demand and other system changes. Commercially available simulation software packages have been used by various mine groups to determine an optimal control philosophy. Satisfactory results were obtained, but the simulations were still based on static control inputs. No simulation system was found that could solve and optimise a system based on real-time instrumentation feedback. By combining simulation capabilities with dynamic control in real time, advanced optimisation could be achieved. Development was done on the theoretical design of the system, where mathematical calculations and the accuracy of the system were evaluated. This study proved that the new controller was viable and, as a result, the development of a fully dynamic control Automation of compressor networks through a dynamic control system iii system incorporating the verified mathematical models followed. All of this was done following a theoretical approach. Intricate control requirements on the supply side were evaluated to determine the impact of new intelligent compressor control strategies. It was found that improved compressor control realised an additional 6.2% electrical energy saving on top of existing savings initiatives. Practical limitations and human perception issues were also addressed. Financial cost-benefit analyses were used to evaluate the viability of using automated compressor control. Ample maintenance data obtained from two leading mining companies was used to evaluate the impact of increased stopping and starting of compressors. Financial cost savings from electrical energy efficiency control strategies were found to considerably outweigh the minimal increase in compressor maintenance. Savings potential on deep-level mines proved to be in the order of 5% of the baseline consumption. When these results are extrapolated to the remaining 22 South African deep-level gold and platinum mines already subjected to demand side management initiatives, potential savings of 12.67 MW can be realised. Based on the Eskom 2014/2015 Megaflex tariff structure, the financial cost saving from 12.67 MW is R61 million. / PhD (Electrical Engineering), North-West University, Potchefstroom Campus, 2015

The generation of compressed air comprises 20% of the total electricity usage in the mining industry, although compressed air is often seen as a free source of energy. There are however significant costs associated with generating compressed air and maintaining a compressed air system. There are several methods to optimise the electricity used to generate compressed air. The focus of this study is on one of these methods – the implementation of a dynamic air compressor selector. A Dynamic Compressor Selector (DCS) system was developed to fulfil this purpose. DCS is a system that combines demand- and supply-side management of a compressed air network. DCS calculates a pressure set point for compressors and schedules the compressors according to the demand from the end-users. End-users include shafts, plants, workshops and smelters. DCS takes all of the compressors and end-users into consideration while doing the calculations. This dissertation focuses on the DCS implementation process and on the problems encountered by previous authors while implementing the DCS technology. Additional problems were encountered while the DCS technology was implemented. DCS was however still successfully implemented. This study will expand the implementation procedure to ensure that the technology can be implemented successfully in the future. DCS was implemented at a platinum mine in South Africa where it was able to calculate pressure set points for the compressors. DCS was able to accurately match the supply of, and demand for compressed air closely, resulting in lower overall compressed air usage. DCS improved compressor scheduling and control, limiting compressor cycling. Improved compressor scheduling and control resulted in significant decreases in the electricity used to generate compressed air at the mine. A target average evening peak clip of 2.197 MW was simulated, set and achieved. Evening peak clip power savings in excess of an average of 3 MW were achieved. / MIng (Mechanical Engineering), North-West University, Potchefstroom Campus, 2015

Compressed air makes up an important part of South African precious metal mining processes. Rising operational costs in the struggling mining sector increased the interest of the power utility, Eskom, and mine management in achievable electrical energy savings. Demand side management initiatives, funded by Eskom, realised a significant improvement in electrical energy efficiency of compressed air networks. Supply side interventions further aided optimisation by lowering operational costs. Previous research identified the need for integrating compressed air supply and demand side initiatives. Automated compressor control systems were needed in industry to realise missed opportunities due to human error on manual control systems. Automatic systems were found to be implemented in the industry, but missed savings opportunities were still encountered. This was due to the static nature of these control systems, requiring human intervention from skilled artisans. A comprehensive system is required that can adjust dynamically to the ever-changing demand and other system changes. Commercially available simulation software packages have been used by various mine groups to determine an optimal control philosophy. Satisfactory results were obtained, but the simulations were still based on static control inputs. No simulation system was found that could solve and optimise a system based on real-time instrumentation feedback. By combining simulation capabilities with dynamic control in real time, advanced optimisation could be achieved. Development was done on the theoretical design of the system, where mathematical calculations and the accuracy of the system were evaluated. This study proved that the new controller was viable and, as a result, the development of a fully dynamic control Automation of compressor networks through a dynamic control system iii system incorporating the verified mathematical models followed. All of this was done following a theoretical approach. Intricate control requirements on the supply side were evaluated to determine the impact of new intelligent compressor control strategies. It was found that improved compressor control realised an additional 6.2% electrical energy saving on top of existing savings initiatives. Practical limitations and human perception issues were also addressed. Financial cost-benefit analyses were used to evaluate the viability of using automated compressor control. Ample maintenance data obtained from two leading mining companies was used to evaluate the impact of increased stopping and starting of compressors. Financial cost savings from electrical energy efficiency control strategies were found to considerably outweigh the minimal increase in compressor maintenance. Savings potential on deep-level mines proved to be in the order of 5% of the baseline consumption. When these results are extrapolated to the remaining 22 South African deep-level gold and platinum mines already subjected to demand side management initiatives, potential savings of 12.67 MW can be realised. Based on the Eskom 2014/2015 Megaflex tariff structure, the financial cost saving from 12.67 MW is R61 million. / PhD (Electrical Engineering), North-West University, Potchefstroom Campus, 2015

Antibiotic resistant bacteria, a growing health crisis, arise due to antibiotic overuse and misuse. Resistant infections endanger the lives of patients and are financially burdensome. Aggregate antimicrobial susceptibility reports, called antibiograms, are critical for tracking antibiotic susceptibility and evaluating the likelihood of the effectiveness of different antibiotics to treat an infection prior to the availability of patient specific susceptibility data. This research leverages the Massachusetts Statewide Antibiogram database, a rich dataset composed of antibiograms for $754$ antibiotic-bacteria pairs collected by the Massachusetts Department of Public Health from $2002$ to $2016$. However, these antibiograms are at least a year old, meaning antibiotics are prescribed based on outdated data which unnecessarily furthers resistance. Our objective is to employ data science techniques on these antibiograms to assist in developing more responsible antibiotic prescription practices. First, we use model selectors with regression-based techniques to forecast the current antimicrobial resistance. Next, we develop an assistant to immediately identify clinically and statistically significant changes in antimicrobial resistance between years once the most recent year of antibiograms are collected. Lastly, we use k-means clustering on resistance trends to detect antibiotic-bacteria pairs with resistance trends for which forecasting will not be effective. These three strategies can be implemented to guide more responsible antibiotic prescription practices and thus reduce unnecessary increases in antibiotic resistance.

Antibiotic resistant bacteria, a growing health crisis, arise due to antibiotic overuse and misuse. Resistant infections endanger the lives of patients and are financially burdensome. Aggregate antimicrobial susceptibility reports, called antibiograms, are critical for tracking antibiotic susceptibility and evaluating the likelihood of the effectiveness of different antibiotics to treat an infection prior to the availability of patient specific susceptibility data. This research leverages the Massachusetts Statewide Antibiogram database, a rich dataset composed of antibiograms for $754$ antibiotic-bacteria pairs collected by the Massachusetts Department of Public Health from $2002$ to $2016$. However, these antibiograms are at least a year old, meaning antibiotics are prescribed based on outdated data which unnecessarily furthers resistance. Our objective is to employ data science techniques on these antibiograms to assist in developing more responsible antibiotic prescription practices. First, we use model selectors with regression-based techniques to forecast the current antimicrobial resistance. Next, we develop an assistant to immediately identify clinically and statistically significant changes in antimicrobial resistance between years once the most recent year of antibiograms are collected. Lastly, we use k-means clustering on resistance trends to detect antibiotic-bacteria pairs with resistance trends for which forecasting will not be effective. These three strategies can be implemented to guide more responsible antibiotic prescription practices and thus reduce unnecessary increases in antibiotic resistance.

The United States (U.S.) Army Yuma Proving Ground (YPG) conducted a series of rotary-wing flight tests for the sole purpose of checking out Telemetry data link instrumentation. Four flights were conducted at YPG in February 2016 that built upon an earlier test flight conducted in June 2015. The most recent iteration of testing examined the benefits of frequency diversity on aircraft and the spatial diversity of receiving sites using existing hardware at YPG. Quantitative analysis from those flight results will be presented and include discussion on how results will affect future mission operations at YPG.

The discharge through a river hydropower plant must be controlled such that the water level at a pre-specified point close to the facility is kept within given bounds. The controllers used today have a somewhat demanding tuning and often create too much amplified, unnatural discharge variations resulting in unsatisfactory control performance.This will affect both surrounding nature and imposing problems for river navigation. This thesis will present a new type of controller called Override Selector feedback Control that adds an estimator for the water levels and water flows in the up- and downriver for each hydropower plant on top of the old controller. The objective of the state feedback control is to keep the total variation of the water levels and the waterflows as small as possible. After the linear, discrete time model of the power plant cascade in a river derived from the Saint Venant equations have been developed, the new concept is evaluated. Both the water level sloshing and the amplification of the discharges compared to the structure used today is damped with the new control structure. Other advantages of the proposed controller is that it will be cost efficient to implement because of the add-on approach. This is seen as a very important factor while the actual benefit that can be made by improving the water level control is very limited and thereby also the will to make extensive control investments. The control structure will be easily implemented as the estimators only need the same input data as used today.

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